In this paper, we introduce new discourse quality metrics and an evaluation method, and furthermore, we provide a pilot implementation and evaluate it in a specific use case – that of public speaking. Voice analysis ...
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The present paper introduces a mathematical model for the cross-talking between microRNA and Protein. Studying the qualitative properties of the proposed model, we infer that the microRNA is an inhibitor for the Prote...
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The hand-eye calibration problem represents a major challenge in robotics, arising from the widespread usage of robotic systems along with robot-mounted sensors. Briefly, consisting of estimating the position and orie...
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Pneumonia is one of the top causes of death in Romania and early detection of this disease improves the recovery chances and shortens the length of hospitalization. In this work, we develop a solution for automatic pn...
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As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around...
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As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors
Representing relevant information from volume data sets is a problem often faced in visualization. Generating meaningful images from highly-complex volume data sets is a challenging, tedious task requiring specialized...
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This paper proposes a RISC-V extension, named SigWavy, meant to optimize the PWM control for general purpose or application specific designs. The RISC-V extension named above is a PWM control Unit with a dedicated ISA...
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Natural Language Processing (NLP) models are one of the most promising topics nowadays. Applications like ChatGPT uncover the power of such models and their broad applications. However, developing such models requires...
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Internet of Things solutions typically involve interaction between sensors, actuators, the cloud, embedded systems and user applications. Often in such cases, there are time constraints specifying the maximum response...
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